Abstract
Outdoor advertisements remain a critical medium for modern marketing, yet accurately verifying billboard text visibility under real-world conditions is still challenging. Traditional Optical Character Recognition (OCR) pipelines excel at cropped text recognition but often struggle with complex outdoor scenes, varying fonts, and weather-induced visual noise. Recently, multimodal Vision-Language Models (VLMs) have emerged as promising alternatives, offering end-to-end scene understanding with no explicit detection step. This work systematically benchmarks representative VLMs - including Qwen 2.5 VL 3B, InternVL3, and SmolVLM2 - against a compact CNN-based OCR baseline (PaddleOCRv4) across two public datasets (ICDAR 2015 and SVT), augmented with synthetic weather distortions to simulate realistic degradation. Our results reveal that while selected VLMs excel at holistic scene reasoning, lightweight CNN pipelines still achieve competitive accuracy for cropped text at a fraction of the computational cost-an important consideration for edge deployment. To foster future research, we release our weather-augmented benchmark and evaluation code publicly.
Abstract (translated)
户外广告仍然是现代营销中的关键媒介，但在现实条件下准确验证广告牌文本的可见性仍然具有挑战性。传统的光学字符识别（OCR）管道在处理裁剪后的文字时表现出色，但对于复杂的户外场景、多变的字体以及天气引起的视觉噪声却往往力不从心。最近，多模态视觉-语言模型（VLMs）作为有前途的替代方案出现，提供了端到端的场景理解能力，并且不需要显式的检测步骤。本研究系统地评估了具有代表性的几种VLM——包括Qwen 2.5 VL 3B、InternVL3和SmolVLM2，在两个公开数据集（ICDAR 2015和SVT）上，这些数据集通过合成天气扭曲进行了扩充以模拟现实中的退化情况。我们的结果显示，虽然所选的VLM在整体场景推理方面表现出色，但轻量级的CNN管道仍然能够实现与裁剪文本相当的准确度，并且计算成本更低——这对于边缘部署来说是一个重要的考虑因素。为了促进未来的研究，我们公开发布了带有天气增强基准和评估代码的数据集。
URL
https://arxiv.org/abs/2507.11730
